Analyzing and Measuring Concentration of Air Pollutants using Advance Statistical Methods

Abstract

Air-pollution has become a global critical concern because of the vast effect on human health and environmental sustainability. By applying traditional and advanced statistical models to analyze the concentrations of PM2.5, PM10, and NO2 etc. Data is collected from the TAQMNS center that is from January 1st, 2018 to December 31st, 2023 across for all five stations of Taiwan. To classify pollution severity and identifying high risk areas traditional statistical methods has been used which finds the standard-deviation, mean, maximum, median, and minimum ranges among the five stations.

An advanced statistical model has been used to give deeper insights on the concentrations of air pollutants dynamics than regular traditional models. Advanced statistical model such as ANOVA is a key contributor to pollution, evaluated variability across stations, and tracked long-term trends. Integration of all five stations integration of meteorological parameters and traffic data enhanced pollutant modeling, highlighting significant correlations with environmental factors. ANOVA approach underscored the influence of weather and traffic on pollution levels. This analysis offers a robust framework for understanding and mitigating air-quality challenges.